7,554 research outputs found
All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles
A novel probabilistic perception algorithm is presented as a real-time joint
solution to data association, object tracking, and object classification for an
autonomous ground vehicle in all-weather conditions. The presented algorithm
extends a Rao-Blackwellized Particle Filter originally built with a particle
filter for data association and a Kalman filter for multi-object tracking
(Miller et al. 2011a) to now also include multiple model tracking for
classification. Additionally a state-of-the-art vision detection algorithm that
includes heading information for autonomous ground vehicle (AGV) applications
was implemented. Cornell's AGV from the DARPA Urban Challenge was upgraded and
used to experimentally examine if and how state-of-the-art vision algorithms
can complement or replace lidar and radar sensors. Sensor and algorithm
performance in adverse weather and lighting conditions is tested. Experimental
evaluation demonstrates robust all-weather data association, tracking, and
classification where camera, lidar, and radar sensors complement each other
inside the joint probabilistic perception algorithm.Comment: 35 pages, 21 figures, 14 table
Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking
We present a data association method for vision-based multiple pedestrian
tracking, using deep convolutional features to distinguish between different
people based on their appearances. These re-identification (re-ID) features are
learned such that they are invariant to transformations such as rotation,
translation, and changes in the background, allowing consistent identification
of a pedestrian moving through a scene. We incorporate re-ID features into a
general data association likelihood model for multiple person tracking,
experimentally validate this model by using it to perform tracking in two
evaluation video sequences, and examine the performance improvements gained as
compared to several baseline approaches. Our results demonstrate that using
deep person re-ID for data association greatly improves tracking robustness to
challenges such as occlusions and path crossings
A bank of unscented Kalman filters for multimodal human perception with mobile service robots
A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints.
In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot.
Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics
Multiple Object Tracking: A Literature Review
Multiple Object Tracking (MOT) is an important computer vision problem which
has gained increasing attention due to its academic and commercial potential.
Although different kinds of approaches have been proposed to tackle this
problem, it still remains challenging due to factors like abrupt appearance
changes and severe object occlusions. In this work, we contribute the first
comprehensive and most recent review on this problem. We inspect the recent
advances in various aspects and propose some interesting directions for future
research. To the best of our knowledge, there has not been any extensive review
on this topic in the community. We endeavor to provide a thorough review on the
development of this problem in recent decades. The main contributions of this
review are fourfold: 1) Key aspects in a multiple object tracking system,
including formulation, categorization, key principles, evaluation of an MOT are
discussed. 2) Instead of enumerating individual works, we discuss existing
approaches according to various aspects, in each of which methods are divided
into different groups and each group is discussed in detail for the principles,
advances and drawbacks. 3) We examine experiments of existing publications and
summarize results on popular datasets to provide quantitative comparisons. We
also point to some interesting discoveries by analyzing these results. 4) We
provide a discussion about issues of MOT research, as well as some interesting
directions which could possibly become potential research effort in the future
A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application
Grid mapping is a well established approach for environment perception in
robotic and automotive applications. Early work suggests estimating the
occupancy state of each grid cell in a robot's environment using a Bayesian
filter to recursively combine new measurements with the current posterior state
estimate of each grid cell. This filter is often referred to as binary Bayes
filter (BBF). A basic assumption of classical occupancy grid maps is a
stationary environment. Recent publications describe bottom-up approaches using
particles to represent the dynamic state of a grid cell and outline
prediction-update recursions in a heuristic manner. This paper defines the
state of multiple grid cells as a random finite set, which allows to model the
environment as a stochastic, dynamic system with multiple obstacles, observed
by a stochastic measurement system. It motivates an original filter called the
probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a
top-down manner. The paper presents a real-time application serving as a fusion
layer for laser and radar sensor data and describes in detail a highly
efficient parallel particle filter implementation. A quantitative evaluation
shows that parameters of the stochastic process model affect the filter results
as theoretically expected and that appropriate process and observation models
provide consistent state estimation results
Visual end-effector tracking using a 3D model-aided particle filter for humanoid robot platforms
This paper addresses recursive markerless estimation of a robot's
end-effector using visual observations from its cameras. The problem is
formulated into the Bayesian framework and addressed using Sequential Monte
Carlo (SMC) filtering. We use a 3D rendering engine and Computer Aided Design
(CAD) schematics of the robot to virtually create images from the robot's
camera viewpoints. These images are then used to extract information and
estimate the pose of the end-effector. To this aim, we developed a particle
filter for estimating the position and orientation of the robot's end-effector
using the Histogram of Oriented Gradient (HOG) descriptors to capture robust
characteristic features of shapes in both cameras and rendered images. We
implemented the algorithm on the iCub humanoid robot and employed it in a
closed-loop reaching scenario. We demonstrate that the tracking is robust to
clutter, allows compensating for errors in the robot kinematics and servoing
the arm in closed loop using vision
PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
Tracking 6D poses of objects from videos provides rich information to a robot
in performing different tasks such as manipulation and navigation. In this
work, we formulate the 6D object pose tracking problem in the Rao-Blackwellized
particle filtering framework, where the 3D rotation and the 3D translation of
an object are decoupled. This factorization allows our approach, called
PoseRBPF, to efficiently estimate the 3D translation of an object along with
the full distribution over the 3D rotation. This is achieved by discretizing
the rotation space in a fine-grained manner, and training an auto-encoder
network to construct a codebook of feature embeddings for the discretized
rotations. As a result, PoseRBPF can track objects with arbitrary symmetries
while still maintaining adequate posterior distributions. Our approach achieves
state-of-the-art results on two 6D pose estimation benchmarks. A video showing
the experiments can be found at https://youtu.be/lE5gjzRKWuAComment: Accepted to RSS 201
SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter
Tracking many cells in time-lapse 3D image sequences is an important
challenging task of bioimage informatics. Motivated by a study of brain-wide 4D
imaging of neural activity in C. elegans, we present a new method of multi-cell
tracking. Data types to which the method is applicable are characterized as
follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to
distinguish cells based only on shape and size, (iii) the number of imaged
cells ranges in several hundreds, (iv) moves of nearly-located cells are
strongly correlated and (v) cells do not divide. We developed a tracking
software suite which we call SPF-CellTracker. Incorporating dependency on
cells' moves into prediction model is the key to reduce the tracking errors:
cell-switching and coalescence of tracked positions. We model target cells'
correlated moves as a Markov random field and we also derive a fast computation
algorithm, which we call spatial particle filter. With the live-imaging data of
nuclei of C. elegans neurons in which approximately 120 nuclei of neurons are
imaged, we demonstrate an advantage of the proposed method over the standard
particle filter and a method developed by Tokunaga et al. (2014).Comment: 14 pages, 6 figure
Statistical Information Fusion for Multiple-View Sensor Data in Multi-Object Tracking
This paper presents a novel statistical information fusion method to
integrate multiple-view sensor data in multi-object tracking applications. The
proposed method overcomes the drawbacks of the commonly used Generalized
Covariance Intersection method, which considers constant weights allocated for
sensors. Our method is based on enhancing the Generalized Covariance
Intersection with adaptive weights that are automatically tuned based on the
amount of information carried by the measurements from each sensor. To quantify
information content, Cauchy-Schwarz divergence is used. Another distinguished
characteristic of our method lies in the usage of the Labeled Multi-Bernoulli
filter for multi-object tracking, in which the weight of each sensor can be
separately adapted for each Bernoulli component of the filter. The results of
numerical experiments show that our proposed method can successfully integrate
information provided by multiple sensors with different fields of view. In such
scenarios, our method significantly outperforms the state of art in terms of
inclusion of all existing objects and tracking accuracy.Comment: 28 pages,7 figure
Self-Driving Cars: A Survey
We survey research on self-driving cars published in the literature focusing
on autonomous cars developed since the DARPA challenges, which are equipped
with an autonomy system that can be categorized as SAE level 3 or higher. The
architecture of the autonomy system of self-driving cars is typically organized
into the perception system and the decision-making system. The perception
system is generally divided into many subsystems responsible for tasks such as
self-driving-car localization, static obstacles mapping, moving obstacles
detection and tracking, road mapping, traffic signalization detection and
recognition, among others. The decision-making system is commonly partitioned
as well into many subsystems responsible for tasks such as route planning, path
planning, behavior selection, motion planning, and control. In this survey, we
present the typical architecture of the autonomy system of self-driving cars.
We also review research on relevant methods for perception and decision making.
Furthermore, we present a detailed description of the architecture of the
autonomy system of the self-driving car developed at the Universidade Federal
do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile
(IARA). Finally, we list prominent self-driving car research platforms
developed by academia and technology companies, and reported in the media
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